Name
Abstract | ||
|---|---|---|
Terrain classification is a crucial feature for mobile robots operating across multiple terrains. One way to learn a terrain classifier is to use a stream of labeled proprioceptive data recorded during a terrain traversal. In this paper, we propose a new terrain classifier that combines a feature extraction from a data stream with the long short-term memory (LSTM) network. Features are extracted from the information-sparse data stream by applying a sliding window computing three central moments. The feature sequence is continuously classified by the LSTM network into multiple terrain classes. Furthermore, a modified bagging method is used to deal with a limited and unbalanced training set. In comparison to the previous work on terrain classifiers for a hexapod crawling robot using only servo-drive feedback, the proposed classifier provides continuous classification with the F1 score up to 0.88, and thus provide better results than SVM classifier learned on the same input data. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01424-7_75 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT III |
Keywords | Field | DocType |
Online classification, Proprioception, Recurrent neural networks | F1 score,Sliding window protocol,Pattern recognition,Computer science,Data stream,Terrain,Recurrent neural network,Feature extraction,Artificial intelligence,Classifier (linguistics),Mobile robot,Machine learning | Conference |
Volume | ISSN | Citations |
11141 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rudolf J. Szadkowski | 1 | 0 | 1.35 |
| Jan Drchal | 2 | 26 | 3.68 |
| Jan Faigl | 3 | 336 | 42.34 |